A couple R projects in which I use empirical simulations to demonstrate key principles of statistics.
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Updated
Aug 25, 2023 - HTML
Simulation refers to the process of creating a virtual model of a real-world system to study its behavior and performance under various conditions. This topic covers the principles, methodologies, and applications of simulation in fields such as engineering, science, healthcare, and social sciences. Simulations can range from simple models to complex, interactive environments, allowing researchers and practitioners to test hypotheses, train individuals, and predict outcomes without the risks or costs associated with real-world experiments. The topic also explores different types of simulation software and tools, as well as best practices for designing and validating simulations.
A couple R projects in which I use empirical simulations to demonstrate key principles of statistics.
How do trait-based food webs rewire when they're subjected to different types of stressors? 📉 📈
Archive of Monte Carlo experiments using the switchboard package for R
A python package to help with quantum espresso calculations. Prepares inputs, parses outputs, performs analysis.
Tesi triennale sulla ricerca del post merger di segnali di onda gravitazionale
In this project, we analyze differences in performance metrics for collegiate basketball teams that have qualified for March Madness versus those that have not using a variety of Monte Carlo Simulation methods in R.
Variety of simulations built using p5.js
Simulation workshop for the Globe Institute, University of Copenhagen
📃 Statistical modeling to adjust for time trends in adaptive platform trials utilizing non-concurrent controls
Obtantion of the complete dynamic model for omnidirectional tire-wheeled robot (Otbot) used to make parameter identification, design a control law and verify both via MATLAB simulations.
The flow solver RHEA solves the conservation equations of fluid motion for compressible flows using MPI combined with CPUs and GPUs
An asset-pricing model using historical prices. Volatility of the asset is modeled as the random variable that changes over time and each iteration. For modelling the future price behavior, Monte Carlo simulations were performed.
📃 Treatment-control comparisons in platform trials including non-concurrent controls
Confidence interval simulations
Analyzing Multicollineaerity with a little simulation
Материалы к курсу "Компьютерная математика", 2020 г.
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